Partnerships & Outreach
The UEF welcomes partnership opportunities with Organizations, Early Adopters, Technical Collaborators, & Academic Researchers who share our stewardship vision.
Paths to Partnership
EIPC and Coherence-Based Collaboration are a public good. The framework is free to adopt, cite, and extend. But the work of translating a framework into sustainable practice — across teams, organizations, and research communities — benefits from direct collaboration. UEF engages partners along three distinct tracks, each with its own rhythm and commitment level.
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For teams, departments, and organizations ready to integrate DignitAI into their existing AI workflows.
This track suits groups who have read the framework, see its potential, and want support translating it into their specific operational context. Typical engagements include organizational adoption strategy consults, team training seminars on DignitAI practice, and the development of customized Project Charter templates for recurring workflows.
Best fit: Enterprises, research groups, editorial teams, or departments where AI has become embedded in daily work but interaction quality has not been formalized.
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For scholars, research institutions, and peer-reviewed venues engaging with DignitAI as an object of study.
This track supports empirical research on DignitAI’s claims, independent replication of the performance findings described in the manuscript, and the development of complementary frameworks in adjacent fields — AI ethics, human-computer interaction, organizational behavior, cognitive science, and philosophy of mind. UEF welcomes citation, critique, and collaborative publication.
Best fit: University researchers, graduate students, and institutional review bodies evaluating AI integration policy.
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For developers, researchers, and engineers interested in extending or stress-testing EIPC and CBC at the technical level.
This track is for those who want to contribute to the framework itself — refining the Project Charter method, testing its performance across models and domains, developing tooling that embeds CBC principles at the software layer, or building on the relational prompting research described in the manuscript. UEF also develops applied systems that embed CBC directly into the user experience, and welcomes technical partners interested in this work.
Best fit: AI practitioners, software developers, and technical researchers with a stake in how frontier models are used, not just built.
From Hykin, S. M. (2026). Emergent Intelligence Performance Calibration (EIPC): Coherence-Based Collaboration for High-Stakes Work with LLMs. Universal Emergence Foundation. (In Prep)
“The difference that CBC makes in Analysis Quality is Measurable. Any time I don’t start with a project Charter, I regret It.”
Clinical Scientist & Early AdopterShare Your Experience
Beyond formal partnership, UEF values hearing from practitioners who have implemented EIPC and CBC in their own work. What changed? What surprised you? Where did the framework need adjusting for your context? Your feedback shapes the next version.
